Multivariable model predictive control of wind turbines based on Laguerre functions

Multivariable model predictive control of wind turbine was considered in this work in full-load regime in order to maximize the power generation and reduce the loads on the equipment. The controller has as inputs the pitch angle and the generator torque and accounts for constraints on both the inputs and the outputs. In order to reduce the computation time and improve the conditioning of the constrained optimization problem, the inputs were parameterization using Laguerre functions. The optimization objective was thus transformed from the identification of the input sequence over the control horizon to the identification of the Laguerre function parameters allowing a good parameterization of the inputs over the control horizon. Multiple linearizing models were employed in order to cover different operating points corresponding to variable wind speeds in the full-load regime zone.